Abstract Number: Esoc2026a366 Deep Learning Model to Predict Treatment Response to Evt in Mdvo Patients: a Post-hoc Distal Trial Analysis
Year: 2026
Type: conference-abstract
Abstract: Background and aims Patients with acute medium/distal vessel occlusions (MDVO) are heterogeneous and two trials failed to establish superiority of endovascular treatment (EVT) over best medical treatment (BMT). Because subgroups showing heterogeneity of the treatment effect (HTE) are unknown, we assessed HTE across strata of outcome predictions from an externally developed model. Methods A TabPFN model was trained on patients of a local stroke registry meeting DISTAL inclusion criteria. The model predicts excellent early neurological outcome (EENO, 24h-NIHSS 0-2) in patients treated with BMT alone using admission clinical and imaging variables. The model was then used to predict EENO in never-seen patients of the DISTAL trial (n=539). HTE of BMT alone vs BMT+EVT was evaluated across strata of predicted and continuous probabilities using interaction analyses with mRS shift as primary outcome. Results In the external development cohort (n=257), 54% of patients showed EENO. The model performed acceptable in the entire DISTAL cohort (AUC 0.72, calibration intercept: −0.13, slope: 0.52, 35% with EENO). There was HTE by predicted probabilities (p-interaction=0.018, Fig1): In patients predicted to have an EENO (n=136), EVT was harmful (cOR 0.50, 95%-CI 0.27-0.90, number needed to harm ≈9 for mRS 0-2, Fig2), while there was no significant effect of EVT in patients predicted not having EENO (n=403). Results of sensitivity analyses were comparable. Conclusions Our deep-learning model identified heterogeneous treatment responses, indicating that EVT may be harmful compared to BMT alone in patients predicted to have an EENO. The model will be made available during ESOC to complement clinical decision-making. Conflict of interest Nothing to disclose Figure 1 - belongs to Results Figure 2 - belongs to Conclusions
Source: European Stroke Journal
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,400
OpenAlex ID: W7160399371
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